Celiac disease gut microbiome studies in the third millennium: reviewing the findings and gaps of available literature - Luz, Vanessa C. C.; Pereira, Sónia Gonçalves (Frontiers in Medical Technology, 2024)
Celiac disease is an autoimmune enteropathy, caused by the ingestion of minute amounts of gluten, in a subset of genetically predisposed individuals. Onset occurs at different ages and with variable symptoms. Gut microbiome may contribute to this variability. Current review intends to provide an overview of the available research on celiac disease gut microbiome and identify the gaps in knowledge that can be useful to guide future studies. Following Preferred Reporting Items for Systematic Reviews and Meta-analysis Protocols-Scoping Review (PRISMA-ScR) guidelines, four electronic databases were searched for literature from January 2000 to July 2023 addressing celiac disease gut microbiome characterization using next-generation sequencing (NGS) approaches. From the 489 publications retrieved, 48 publications were selected and analysed, focusing on sample characterization (patients, controls and tissues) and methodologies used for NGS microbiome analysis and characterization. Majority of the selected publications regarded children and adults, and 4 were randomized clinical trials. Number of participants per study greatly varied and were typically low. Feces were the most frequently tested sample matrix and duodenal samples were analysed in one third of the studies. Incomplete and diverse information on the methodological approaches and gut microbiome results was broadly observed. While similar trends regarding some phyla relative abundance, like Pseudomonadota (former Proteobacteria), were detected in some studies, others contradicted those results. The observed high variability of technical approaches and possibly low power and sample sizes may prevent reaching a consensus on celiac disease gut microbiome composition. Standardization of research protocols to allow reproducibility and comparability is required, as interdisciplinary collaborations to further data analysis, interpretation and, more importantly, health outcomes prediction or improvement.